Prototype selection for dissimilarity-based classifiers

A conventional way to discriminate between objects represented by dissimilarities is the nearest neighbor method. A more efficient and sometimes a more accurate solution is offered by other dissimilarity-based classifiers. They construct a decision rule based on the entire training set, but they need just a small set of prototypes, the so-called representation set, as a reference for classifying new objects. Such alternative approaches may be especially advantageous for non-Euclidean or even non-metric dissimilarities. The choice of a proper representation set for dissimilarity-based classifiers is not yet fully investigated. It appears that a random selection may work well. In this paper, a number of experiments has been conducted on various metric and non-metric dissimilarity representations and prototype selection methods. Several procedures, like traditional feature selection methods (here effectively searching for prototypes), mode seeking and linear programming are compared to t...

• Type: Journal
• Year: 2006
• Where: PR
• Authors: Elzbieta Pekalska, Robert P. W. Duin, Pavel Paclík
• Keywords: Dissimilarity-based Classifiers, Entire Training, PR 2006, Random Selection